ClickRank: Learning Session-Context Models to Enrich Web Search Ranking
ClickRank: Learning Session-Context Models to Enrich Web Search Ranking
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DOI：
10.1145/2109205.2109206
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
Guangyu Zhu;G. Mishne
中科院分区：
文献类型：
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作者：
Guangyu Zhu;G. Mishne
用户浏览信息，特别是与搜索无关的活动，揭示了有关Web用户偏好和意图的重要上下文信息。在这篇文章中，我们论证了挖掘一般Web用户行为数据对提高排名和其他Web搜索体验的重要性，重点是分析单个用户会话以创建聚合模型。在此背景下，我们介绍了ClickRank，一种高效的、可伸缩的算法，用于从一般的Web用户行为数据中估计网页和网站的重要性。给出了基于故意冲浪者模型的ClickRank的理论基础，并讨论了它的性质。我们定量地评估了它在网络搜索排名问题上的有效性，表明它作为一种新的网络搜索特征对检索性能有很大的贡献。我们证明了ClickRank用于网络搜索排名的结果与其他方法产生的结果具有很强的竞争力，但实现了更好的可扩展性和更低的计算成本。最后，我们讨论了ClickRank在提供丰富的用户Web搜索体验方面的新应用，强调了我们的方法对于非排名任务的有效性。
User browsing information, particularly non-search-related activity, reveals important contextual information on the preferences and intents of Web users. In this article, we demonstrate the importance of mining general Web user behavior data to improve ranking and other Web-search experience, with an emphasis on analyzing individual user sessions for creating aggregate models. In this context, we introduce ClickRank, an efficient, scalable algorithm for estimating Webpage and Website importance from general Web user-behavior data. We lay out the theoretical foundation of ClickRank based on an intentional surfer model and discuss its properties. We quantitatively evaluate its effectiveness regarding the problem of Web-search ranking, showing that it contributes significantly to retrieval performance as a novel Web-search feature. We demonstrate that the results produced by ClickRank for Web-search ranking are highly competitive with those produced by other approaches, yet achieved at better scalability and substantially lower computational costs. Finally, we discuss novel applications of ClickRank in providing enriched user Web-search experience, highlighting the usefulness of our approach for nonranking tasks.